MSGID: 1:317/3 64acdaee   
   PID: hpt/lnx 1.9.0-cur 2019-01-08   
   TID: hpt/lnx 1.9.0-cur 2019-01-08   
    LIONESS redefines brain tissue imaging    
    Large collaboration at ISTA yields an unprecedented 'live' view into the   
   brain's complexity    
      
     Date:   
         July 10, 2023   
     Source:   
         Institute of Science and Technology Austria   
     Summary:   
         Scientists have come together to present a new way to observe the   
         brain's structure and dynamics -- in a high resolution and without   
         damaging the tissue.   
      
      
         Facebook Twitter Pinterest LinkedIN Email   
      
   ==========================================================================   
   FULL STORY   
   ==========================================================================   
   Brain tissue is one of the most intricate specimens that scientists have   
   arguably ever dealt with. Packed with currently immeasurable amount of   
   information, the human brain is the most sophisticated computational   
   device with its network of around 86 billion neurons. Understanding   
   such complexity is a difficult task, and hence making progress requires   
   technologies to unravel the tiny, complex interactions taking place in   
   the brain at microscopic scales.   
      
   Imaging is therefore an enabling tool in neuroscience.   
      
   The new imaging and virtual reconstruction technology developed by Johann   
   Danzl's group at ISTA is a big leap in imaging brain activity and is   
   aptly named LIONESS -- Live Information Optimized Nanoscopy Enabling   
   Saturated Segmentation. LIONESS is a pipeline to image, reconstruct,   
   and analyze live brain tissue with a comprehensiveness and spatial   
   resolution not possible until now.   
      
   "With LIONESS, for the first time, it is possible to get a comprehensive,   
   dense reconstruction of living brain tissue. By imaging the tissue   
   multiple times, LIONESS allows us to observe and measure the dynamic   
   cellular biology in the brain take its course," says first author Philipp   
   Velicky. "The output is a reconstructed image of the cellular arrangements   
   in three dimensions, with time making up the fourth dimension, as the   
   sample can be imaged over minutes, hours, or days," he adds.   
      
   With LIONESS neuroscientists can image living brain tissue and achieve   
   high- resolution 3D imagery without damaging the living sample.   
      
   Collaboration and AI the Key The strength of LIONESS lies in refined   
   optics and in the two levels of deep learning -- a method of Artificial   
   Intelligence -- that make up its core: the first enhances the image   
   quality and the second identifies the different cellular structures in   
   the dense neuronal environment.   
      
   The pipeline is a result of a collaboration between the Danzl group,   
   Bickel group, Jonas group, Novarino group, and ISTA's Scientific Service   
   Units, as well as other international collaborators. "Our approach was   
   to assemble a dynamic group of scientists with unique combined expertise   
   across disciplinary boundaries, who work together to close a technology   
   gap in the analysis of brain tissue," Johann Danzl of ISTA says.   
      
   Surpassing hurdles Previously it was possible to get reconstructions of   
   brain tissue by using Electron Microscopy. This method images the sample   
   based on its interactions with electrons. Despite its ability to capture   
   images at a few nanometers -- a millionth of a millimeter -- resolution,   
   Electron Microscopy requires a sample to be fixed in one biological state,   
   which needs to be physically sectioned to obtain 3D information. Hence,   
   no dynamic information can be obtained.   
      
   Another previously known technique of Light Microscopy allows observation   
   of living systems and record intact tissue volumes by slicing them   
   "optically" rather than physically. However, Light Microscopy is severely   
   hampered in its resolving power by the very properties of the light waves   
   it uses to generate an image. Its best-case resolution is a few hundred   
   nanometers, much too coarse-grained to capture important cellular details   
   in brain tissue.   
      
   Using Super-resolution Light Microscopy scientists can break   
   this resolution barrier. Recent work in this field, dubbed SUSHI   
   (Super-resolution Shadow Imaging), showed that applying dye molecules   
   to the spaces around cells and applying the Nobel Prize-winning   
   super-resolution technique STED (Stimulated Emission Depletion) microscopy   
   reveals super-resolved 'shadows' of all the cellular structures and thus   
   visualizes them in the tissue. Nevertheless, it has been impossible to   
   image entire volumes of brain tissue with resolution enhancement that   
   matches the brain tissue's complex 3D architecture. This is because   
   increasing resolution also entails a high load of imaging light on the   
   sample, which may damage or 'fry' the subtle, living tissue.   
      
   Herein lies the prowess of LIONESS, having been developed for,   
   according to the authors, "fast and mild" imaging conditions, thus   
   keeping the sample alive. The technique does so while providing   
   isotropic super-resolution -- meaning that it is equally good in all   
   three spatial dimensions -- that allows visualization of the tissue's   
   cellular components in 3D nanoscale resolved detail.   
      
   LIONESS collects only as little information from the sample as needed   
   during the imaging step. This is followed by the first deep learning step   
   to fill in additional information on the brain tissue's structure in a   
   process called Image Restoration. In this innovative way, it achieves   
   a resolution of around 130 nanometers, while being gentle enough for   
   imaging of living brain tissue in real-time. Together, these steps   
   allow for a second step of deep learning, this time to make sense of the   
   extremely complex imaging data and identify the neuronal structures in   
   an automated manner.   
      
   Homing In "The interdisciplinary approach allowed us to break the   
   intertwined limitations in resolving power and light exposure to the   
   living system, to make sense of the complex 3D data, and to couple   
   the tissue's cellular architecture with molecular and functional   
   measurements," says Danzl.   
      
   For virtual reconstruction, Danzl and Velicky teamed up with visual   
   computing experts: the Bickel group at ISTA and the group led by   
   Hanspeter Pfister at Harvard University, who contributed their expertise   
   in automated segmentation - - the process of automatically recognizing   
   the cellular structures in the tissue -- and visualization, with further   
   support by ISTA's image analysis staff scientist Christoph Sommer. For   
   sophisticated labeling strategies, neuroscientists and chemists from   
   Edinburgh, Berlin, and ISTA contributed.   
      
   Consequently, it was possible to bridge functional measurements, i.e. to   
   read out the cellular structures together with biological signaling   
   activity in the same living neuronal circuit. This was done by imaging   
   Calcium ion fluxes into cells and measuring the cellular electrical   
   activity in collaboration with the Jonas group at ISTA. The Novarino group   
   contributed human cerebral organoids, often nicknamed mini-brains that   
   mimic human brain development. The authors underline that all of this   
   was facilitated through expert support by ISTA's top-notch scientific   
   service units.   
      
   Brain structure and activity are highly dynamic; its structures evolve   
   as the brain performs and learns new tasks. This aspect of the brain   
   is often referred to as "plasticity." Hence, observing the changes in   
   the brain's tissue architecture is essential to unlocking the secrets   
   behind its plasticity. The new tool developed at ISTA shows potential for   
   understanding the functional architecture of brain tissue and potentially   
   other organs by revealing the subcellular structures and capturing how   
   these might change over time.   
      
       * RELATED_TOPICS   
             o Mind_&_Brain   
                   # Brain-Computer_Interfaces # Brain_Injury # Intelligence   
             o Matter_&_Energy   
                   # Medical_Technology # Optics # Biochemistry   
             o Computers_&_Math   
                   # Neural_Interfaces # Computer_Graphics # Communications   
       * RELATED_TERMS   
             o Scanning_electron_microscope o Conflict_resolution o Amygdala   
             o Perfectionism_(psychology) o Homosexuality o Alpha_wave o   
             Brain_damage o Aggression   
      
   ==========================================================================   
      
    Print   
      
    Email   
      
    Share   
   ==========================================================================   
   ****** 1 ****** ***** 2 ***** **** 3 ****   
   *** 4 *** ** 5 ** Breaking this hour   
   ==========================================================================   
       * Six_Foods_to_Boost_Cardiovascular_Health   
       * Cystic_Fibrosis:_Lasting_Improvement *   
       Artificial_Cells_Demonstrate_That_'Life_...   
      
       * Advice_to_Limit_High-Fat_Dairy_Foods_Challenged   
       * First_Snapshots_of_Fermion_Pairs *   
       Why_No_Kangaroos_in_Bali;_No_Tigers_in_Australia   
       * New_Route_for_Treating_Cancer:_Chromosomes *   
       Giant_Stone_Artefacts_Found:_Prehistoric_Tools   
       * Astonishing_Secrets_of_Tunicate_Origins *   
       Most_Distant_Active_Supermassive_Black_Hole   
      
   Trending Topics this week   
   ==========================================================================   
   SPACE_&_TIME Jupiter Mars NASA MATTER_&_ENERGY Materials_Science   
   Construction Engineering_and_Construction COMPUTERS_&_MATH   
   Artificial_Intelligence Educational_Technology Neural_Interfaces   
      
      
   ==========================================================================   
      
   Strange & Offbeat   
   ==========================================================================   
   SPACE_&_TIME   
   Quasar_'Clocks'_Show_Universe_Was_Five_Times_Slower_Soon_After_the_Big_Bang   
   First_'Ghost_Particle'_Image_of_Milky_Way   
   Gullies_on_Mars_Could_Have_Been_Formed_by_Recent_Periods_of_Liquid_Meltwater,   
   Study_Suggests MATTER_&_ENERGY   
   Bees_Make_Decisions_Better_and_Faster_Than_We_Do,_for_the_Things_That_Matter_to   
   Them   
   These_Lollipops_Could_'Sweeten'_Diagnostic_Testing_for_Kids_and_Adults_Alike   
   Holograms_for_Life:_Improving_IVF_Success COMPUTERS_&_MATH   
   Number_Cruncher_Calculates_Whether_Whales_Are_Acting_Weirdly   
   AI_Tests_Into_Top_1%_for_Original_Creative_Thinking   
   Researchers_Create_Highly_Conductive_Metallic_Gel_for_3D_Printing   
   Story Source: Materials provided by   
   Institute_of_Science_and_Technology_Austria. Note: Content may be edited   
   for style and length.   
      
      
   ==========================================================================   
   Journal Reference:   
      1. Philipp Velicky, Eder Miguel, Julia M. Michalska, Julia Lyudchik,   
      Donglai   
         Wei, Zudi Lin, Jake F. Watson, Jakob Troidl, Johanna Beyer,   
         Yoav Ben- Simon, Christoph Sommer, Wiebke Jahr, Alban Cenameri,   
         Johannes Broichhagen, Seth G. N. Grant, Peter Jonas, Gaia Novarino,   
         Hanspeter Pfister, Bernd Bickel, Johann G. Danzl. Dense 4D nanoscale   
         reconstruction of living brain tissue. Nature Methods, 2023; DOI:   
         10.1038/s41592-023- 01936-6   
   ==========================================================================   
      
   Link to news story:   
   https://www.sciencedaily.com/releases/2023/07/230710113914.htm   
      
   --- up 1 year, 19 weeks, 10 hours, 50 minutes   
    * Origin: -=> Castle Rock BBS <=- Now Husky HPT Powered! (1:317/3)   
   SEEN-BY: 15/0 106/201 114/705 123/120 153/7715 218/700 226/30 227/114   
   SEEN-BY: 229/110 112 113 307 317 400 426 428 470 664 700 291/111 292/854   
   SEEN-BY: 298/25 305/3 317/3 320/219 396/45 5075/35   
   PATH: 317/3 229/426